handling corrupted records in spark pyspark databricks

handling corrupted records in apache spark, especially when using pyspark in databricks, is a common task when dealing with large datasets. corrupted records can arise from various sources, such as malformed json files, missing fields, or incorrect data types. apache spark provides built-in mechanisms to handle these situations gracefully.

tutorial: handling corrupted records in pyspark on databricks

step 1: setting up your databricks environment

1. **create a databricks workspace**: if you haven't done so, create a databricks workspace and a cluster.
2. **create a notebook**: start a new notebook where you’ll write your pyspark code.

step 2: sample data creation

to illustrate how to handle corrupted records, let's create a sample dataframe with some corrupted records.

```python

initialize spark session

create a sample dataframe with corrupted records
data = [
('john', 30),
('jane', 'notaninteger'), corrupted record
('doe', 25),
('alice'), corrupted record (missing age)
('bob', 40)
]

define schema
schema = structtype([
structfield("name", stringtype(), true),
structfield("age", integertype(), true)
])

create dataframe

show the original dataframe
```

**expected output:**
```
+-----+----+
| name| age|
+-----+----+
| john| 30 |
| jane| notaninteger|
| doe| 25 |
|alice|null|
| bob| 40 |
+-----+----+
```

step 3: handling corrupted records

in spark, you can use the `mode` option when reading data to specify how to handle corrupted records. the common modes are:

- `permissive`: (default) ignores corrupted records and sets the column values to `null`.
- `dropmalformed`: drops any rows that are corrupted.
- `failfast`: throws an error when it encounters corrupted records. ...

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handling corrupted records
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